Algorithmic fairness plays an increasingly critical role in machine learning research. Several group fairness notions and algorithms have been proposed. However, the fairness guarantee of existing fair classification methods mainly depends on specific data distributional assumptions, often requiring large sample sizes, and fairness could be violated when there is a modest number of samples, which is often the case in practice. In this paper, we propose FaiREE, a fair classification algorithm that can satisfy group fairness constraints with finite-sample and distribution-free theoretical guarantees. FaiREE can be adapted to satisfy various group fairness notions (e.g., Equality of Opportunity, Equalized Odds, Demographic Parity, etc.) and achieve the optimal accuracy. These theoretical guarantees are further supported by experiments on both synthetic and real data. FaiREE is shown to have favorable performance over state-of-the-art algorithms.

翻译：算法公平性在机器学习研究中扮演着越来越关键的角色。已有多种群体公平性概念和算法被提出。然而，现有公平分类方法的公平性保证主要依赖于特定的数据分布假设，通常需要大量样本，而在实际中常见的小样本情况下，公平性可能被违背。本文提出FaiREE，一种能够在有限样本和无分布理论保证下满足群体公平性约束的公平分类算法。FaiREE可适配多种群体公平性概念（例如，机会均等、均等化赔率、人口统计均等性等）并实现最优准确率。这些理论保障得到了合成数据和真实数据实验的进一步支持。实验表明，FaiREE的性能优于当前最先进的算法。